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Policy Risk, Distance and Private Participation Projects in Latin America

Abstract: In this paper we investigate how differences in policy risk levels between the home

and the host country affect private participation projects. Traditionally, distance has been

associated to obstacles and challenges, adversely affecting investments. Yet, a recent stream

of the literature has emphasized the potential positive effects of distance. Drawing on a

sample of 3,971 projects from 1990 to 2013 in 21 Latin American host countries from 47

home countries, we find a strong positive effect for higher distance in absolute terms.

However, our results also show that distance in nominal terms has no significant effect,

pointing to a potential confounding effect between positive and negative distance.

Keywords: Policy Risk; Distance; Latin America; Infrastructure Projects; Corporate

Political Strategy

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Policy Risk, Distance and Private Participation Projects in Latin America

1. Introduction

Historically, private participation projects were largely unavailable to foreign

investors as governments were reluctant to let multinational firms take significant ownership

stakes in this kind of projects which, according to the World Bank, refer to partially privatized

infrastructure projects where private domestic or multinational enterprises play a significant

role (Jiang et al. 2013). However, a dramatic shift has occurred in the last two decades and

now private ownership in infrastructure development has notably risen as a result of most

governments not only allowing but even actively seeking foreign investors (Henisz et al.

2005).

Private participation projects have indeed caught the attention of researchers, and

various papers have been published on the role of the state as an owner (Doh, 2000; Doh et

al. 2004; Inoue, 2013), government credibility (Ramamurti, 2003), the privatization method

(Djankov, 1999) or host country reforms (Henisz et al. 2005). Recently, Jiang et al. (2013)

have called the attention on the role of policy risk in the host country as one of the critical

factors affecting the success of private participation projects.

Policy risk refers to “the risk that a government will opportunistically alter policies

to directly or indirectly expropriate a firm’s profits or assets” (Holburn and Zelner, 2010, p.

1290). In other words, it accounts for the degree to which policy makers may unilaterally

alter the terms and conditions governing firms and market transactions in a country (Henisz,

2000). As a component at the micro level of the broader concept of political risk, policy risk

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affects only given companies or industries rather than the whole territory (Oetzel, 2005; Alon

and Herber, 2009).

Yet, the role of distance between the host and the home country in policy stability has

been largely overlooked. Besides, the vast majority of previous studies on private

participations projects have focused on developed countries, with a few on emerging

economies such as India and China (Rammamurti and Doh, 2004).

However, and despite recent trends of liberalization (Anand et al. 2006), the Latin

American region has been so far ignored. This is in fact striking given the particular

characteristics of many Latin American countries, where the levels of policy risk due to

instability are relatively high (Fenmore and Volgy, 1978; Fatehi, 1994; García-Canal and

Guillén, 2008; Blanco and Grier, 2009). Foreign investment in utility and infrastructure

projects played a key role in the development of Latin America until the 1929 Great

Depression. Sectors most benefited were construction of railroads, electrical facilities,

ports, telecommunication and energy. However, following an import-substitution

industrialization model, most Latin American states gradually replaced foreign investors.

Policy risk peaked in the 60s and 70s when governments of many countries took over the

properties of several MNEs in utility sectors (Bucheli and Salvaj, 2014). Governments

maintained their leading role as the main infrastructure developer until the 90s, when foreign

investors regain their importance.

This paper aims to take one step further and address both gaps in the literature by

analyzing the role of distance in policy stability between the home and the host country.

More specifically, we seek to answer the following research questions: 1) How does the

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home-host distance in policy stability affect private participation projects’ success? and 2)

are there differences between the effects of distance between the host and the home country

in nominal and in absolute terms? To do so, we analyze a dataset of 3,971 projects from

1990 to 2013 in 21 Latin American host countries from 47 home countries and find a strong

positive association between the home-host distance in absolute terms and the probability of

success of the project. However, we do not find any statistically significant effect for home-

host distance in nominal terms.

By addressing these research questions, we aim to contribute to the literature on

private participation projects by showing that the success of these projects is in fact

associated to the differences between the home and the host countries in terms of policy risk,

and not that much to the host-country level. Besides, we also aim contribute to the growing

literature on non-market strategy and, more specifically, on the proactive approach to

political and policy risk (Bucheli and Salvaj, 2009; García-Canal and Guillén, 2008; Holburn

and Zelner, 2010; Jiménez, 2011; Jiménez et al. 2014), by showing that higher distance in

policy risk is actually associated to a higher probability of success in our geographical setting.

Last, but not least, the paper also aims to make a contribution to the literature on distance and

international management (Zaheer et al. 2012), by highlighting the very different effects that

the concept of distance may have depending on the specific conceptualization of distance

chosen in the research design (Hernandez and Nieto, 2015; Trapczynski and Banalieva, 2016)

and, more specifically, by showing the different effects of distance in nominal and in absolute

terms.

The paper is structured as follows: we review the literature and set out our hypotheses

in section two. In section three, we describe our sample, variables, statistical estimation

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technique and collinearity diagnosis. In section four we describe our results. Finally, in

section five we discuss the findings, limitations and future avenues for research.

2. Literature Review and Hypotheses

The notion of distance is employed to refer to the differences between two countries

(Hakanson and Ambos, 2010). It has a pivotal role on international business research

(Ambos and Hakanson, 2014; Hutzschenreuter et al. 2014) and it has even claimed that

international management is essentially the management of distance (Zaheer et al. 2012).

The majority of the literature assume and association between distance and higher

challenges for investments (Berry et al. 2010). For instance, Hakanson and Ambos ((2010,

p. 195) argue that “the general assumption in most studies is that the more different a foreign

environment is as compared to that of a firm's (or an individual's) country of origin, the more

difficult it will be to collect, analyse and correctly interpret information about it, and the

higher are therefore the uncertainties and difficulties – both expected and actual – of doing

business there”.

However, a recent trend in the literature has started to emerge pointing out the

potential benefits of distance. Thus, distance can be seen as an opportunity rather than a

challenge and researchers should avoid falling into the trap that higher distance

systematically engenders negative outcomes (Tung and Verbeke, 2010; Stahl and Tung,

2014; Stahl et al. 2016). In fact, there are various reasons why distance can potentially

advantageous for investments. First, according to the so-called “inverse resonance” (Carr et

al. 2001; Yildiz and Fey, 2016), sometimes individuals or firms from more distant locations

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received a more positive (or at least neutral) reception in the host country due to xenophile

reasons. Second, distance can also offer opportunities for arbitrage, complementarity or

creative diversity (Ghemawat, 2001, 2003; Shenkar et al. 2008; Zaheer et al. 2012). Third,

distance can develop new capabilities and get access to resources (including knowledge and

learning sources) that are unavailable in closer countries (Nachum et al. 2008). Overall,

distance can provide advantages in multiple domains ranging from innovation, operating

costs, human capital, networks, image, etc (for a review, see the recent contribution by

Edman (2016)).

This notion of distance as a potential opportunity rather than a threat parallels another

stream of literature focusing on the proactive approach of some firms towards political and

policy risk. While in general firms are reluctant to invest in locations where the levels of risk

are high, some studies have empirically shown that some firms behave differently and,

instead, tend to invest in risky locations (García-Canal and Guillén, 2008; Holburn and

Zelner, 2010; Jiménez, 2011). Building on the resource and capability-based view of the firm

(Wernerfelt, 1984; Barney, 1991) and on the corporate political activity and non-market

strategy literature (Hillman and Hitt, 1999; Hillman et al. 2004; Bonardi et al. 2006;

Holtbrügge et al. 2007; Oliver and Holzinger, 2008; Doh et al. 2012), this stream of literature

argue that some firms develop political capabilities1 that allow them to achieve great

efficiency at assessing the real level of risk, negotiating with governments, lobbying, creating

political coalitions and networks, identifying the best way to align with the government´s

1 Political capabilities can be defined as: “organizational and strategic activities by which senior or acting

representatives reconfigure, leverage and release political resources to achieve new resource configurations that

enable the company to adapt to, anticipate or even shape changes in the corporate political environment”

(Lawton et al. 2013, p. 230).

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interests or even bribing (Holburn, 2001; Wan, 2005; García-Canal and Guillén, 2008;

Bucheli and Salvaj, 2009; Jiménez, 2010).

Given the relatively high levels of political discretion and policy risk in Latin

America, and the particular nature of private participations projects, characterized by a high

public visibility, government involvement and even nationalistic pressures from public

opinion and special interest groups (Newman 2000), we argue that this type of investments

is particularly apt for adopting a proactive approach towards policy risk. In fact, a substantial

part of the literature on political capabilities has focused on the electricity sector (Holburn,

2001; Holburn and Zelner, 2010), the air transport sector (Lawton and Rajwani, 2011;

Lawton et al. 2013), or regulated industries (García-Canal and Guillén, 2008; Jiménez, 2014)

as the exposure to the specific threat of policy risk and the frequent interactions with

authorities trigger the organizational learning mechanism that transform routines into

knowledge and capabilities (Adger, 2000; Dai et al. 2013).

We therefore expect that investors from politically stable (usually developed)

countries find in these locations opportunities to take advantage of their political capabilities

and obtain preferential treatment in those countries where the host government have greater

discretion power which, in turn, provide better conditions for the success of the project.

However, when the distance is negative (indicating investors from more unstable countries

than the Latin American region), we also expect greater distance to have a positive effect. In

this case, larger distance between the home country and the home country indicates that the

investor comes from an environment with a very high level of policy risk, and the host

country can offer a safer environment for the correct development of the project.

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We therefore propose the following hypothesis:

Hypothesis 1: A higher absolute distance in policy risk levels between the host country and

the home country have a positive effect on the success of private participation projects in

Latin America.

While in the previous hypothesis we argue that distance in absolute terms could be

associated with a higher probability of success in private participation projects in Latin

America, we must note that distance has also been frequently operationalized in nominal and

not only in absolute terms. However, we claim that distance in nominal terms does not take

into a potential confounding effect between positive and negative distances (Hernandez and

Nieto, 2015). By treating cases in which the score of policy risk in the home country is higher

than that of the home country as a “`+” and cases in which the score of policy risk in the

home country is lower than that of the home country as a “`-”, nominal distance puts large

differences between home and host countries as two end of a continuum and small differences

in the middle. In contrast, absolute difference puts large differences as one of the ends of the

spectrum, and small differences on the other.

As a result, nominal distance positive and negative effects may compensate each

other, making the overall effect of distance negligible. In fact, in a recent paper Trapczynski

and Banalieva (2016) propose that not addressing correctly the issue of direction is the main

cause of the contradictory results in the literature devoted to the relationship between

institutional distance and firm performance, with some authors finding a negative

relationship due to increased uncertainty and transaction costs (Ghemawat, 2001; Dow and

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Ferencikova, 2010) and others findings a positive one due to increased awareness of potential

problems and better decision-making process (Nachum et al. 2008; Dikova, 2009).

We therefore propose the following hypothesis:

Hypothesis 2: As positive and negative distance between host and home countries offset each

other, a higher nominal distance in policy risk levels has an ambiguous effect on the success

of private participation projects in Latin America.

3. Method

3.1. Sample

In order to analyze the role of policy risk distance in private participation projects in

Latin America, we collected data from the World Bank´s Private Participation in

Infrastructure (PPI) dataset. This dataset has been previously employed to study private

participation projects (see for instance Jiang et al. 2015). We collected information on all

projects located in Latin America for which data on policy risk was available. Overall, we

found 3,971 projects ranging from 1990 to 2013. The list of host (21) and home (47) countries

covered in our sample are included in Tables 1a and 1b. The greatest numbers of projects are

located in Brazil, Argentina, Mexico and Peru. The lion´s share of home countries of the

main investors of the projects corresponds to Brazil, Spain, Mexico and the United States.

*** Insert Tables 1a and 1b about here ***

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3.2. Variables

3.2.1. Dependent variable

We assess the success of a project through a binary measure. The PPI database

distinguishes between six types of potential outcomes: “under construction”, “operational”,

“merged”, “concluded”, “cancelled” or “distressed” (when the government or the operator

has either requested termination or is under international arbitration). Following previous

research (Jiang et al. 2015), we consider the first four categories as successful (coded 1) and

the latter two as failed (coded 0).

3.2.2. Independent Variable

To measure policy risk, we rely on the Political Constraints Index (POLCONV) by

Henisz (1998). This index has been frequently employed in the literature (see, for instance,

García-Canal and Guillén, 2008; Holburn and Zelner, 2010 or Jiménez et al. 2014), as it

captures a critical component for investments, which is political discretion and the credibility

of a government to maintain policies unchanged. The index measures the number of

independent political branches able to veto government, reflecting how easily a government

can modify the existing status quo and change laws and regulations arbitrarily. Thus, in

countries where the number of independent political veto points is high, the likelihood of

arbitrary policy changes is lower. In contrast, “systems in which policymaking authority is

more concentrated, or is shared among actors with similar preferences, are characterized

by lower policy stability and thus pose a higher level of policy risk” (Holburn and Zelner,

2010, p. 6).

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To calculate the distance between the host and the home country, we subtract the

corresponding score of POLCONV of the host country from the one for the home country of

the main investor and we also calculate the absolute value of such subtraction.

3.2.3. Control Variables

We also include several control variables in our model. First, at the project-level, we

include a dummy variable for projects including at least one local investor. We also control

with another dummy variable whether the main investor of the project is a foreign company.

We include three more dummy variables to control for greenfield versus brownfield projects,

publicly traded projects and projects with host government participation. Finally, we also

include the project´s total investment (log), age, sector and delay of the project. The age of

the project measures the time lapsed from when the project was set up until the final year

covered in the sample. Since some projects are more recent than others, it is important to

control for this variable in order to minimize this potential bias (Jiang et al. 2015). Regarding

sector, the PPI distinguishes between energy, telecommunication, transport, and water

sewerage projects. We include the first three and use the fourth one as the category of

reference. Finally, delay controls for the time lag between the project commitment and

closure.

Second, at the country-level, we control for the GDP (log), GDP growth, GDP per

capita (log) and unemployment rate of the host country. We also include the host-country

level of POLCONV to control for the level of risk in addition to the distance between the

home and the host country. Table 2 reports the descriptive statistics of the variables included

in the model.

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*** Insert Table 2 about here ***

3.3. Statistical estimation technique

We rely on logistic regression to account for the binary nature of our dependent

variable. It is important to note that limited dependent variable models subject to non-

linearity, which means that the estimated coefficients are not equal to the marginal effects

and therefore not appropriate to interpret the size of the effect (Wieserma and Bowen, 2009).

In order to interpret the size of the effect, it is necessary to estimate the average marginal

effects (Boellis et al. 2016).

3.4. Collinearity Diagnosis

Tables 3a and 3b shows the two correlation matrices and the Variance Inflation

Factors (VIFs). Since the correlations are relatively low and all VIFs are below the limit of

10 recommended by Neter et al. (1985), Kennedy (1992) and Studenmund (1992), and also

the stricter limit of 5.3 proposed by Hair et al. (1999), we can affirm that no problems of

collinearity affect the sample.

*** Insert Tables 3a and 3b about here ***

4. Results

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Table 4 reports the logistic regression results of the two models. Model 1 includes the

difference in POLCONV scores between the home and the host country in absolute terms

whereas Model 2 includes the difference in nominal terms.

*** Insert Table 4 about here ***

In Hypothesis 1 we proposed that a broader distance in absolute terms should be

associated to a higher probability of success for private participation projects in Latin

America. The coefficient of the POLCONV distance is indeed positive and significant (β =

1.130, p = 0.018). As expected, investors from home countries with low levels of policy risk

pursue a proactive approach towards policy risk and have better chances of success in

countries where the government discretion is large enough to provide advantageous

conditions. Conversely, investors from home countries with higher levels of policy risk can

find safer investment opportunities than in their home countries. Notably, it is the distance

in POLCONV scores and not the POLCONV score of the host country the variable that is

significant, confirming the relevance of distance between the home and host countries as the

critical variable affecting private participation projects.

However, as previously mentioned, average marginal effects need to be estimated to

assess the size of the effect. Table 5 reports the average marginal effects and shows that when

the absolute difference in POLCONV between the host and the home country increases by

1, the likelihood of success of the private participation project increases by 7% (p = 0.018).

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*** Insert Table 5 about here ***

In Hypothesis 2 we proposed that a broader distance in nominals terms should not be

associated with the probability of success for private participation projects in Latin America.

The coefficient of the POLCONV distance is indeed not significant (β = -0.0800, p > 0.10).

This result indicates that, as expected, the effect of positive and negative distance offset each

other.

Regarding the control variables, the results show that the three sectors included in the

models are associated with higher probabilities of success compared to the reference category

(water sewerage projects). Age, delay, host country GDP and unemployment (probably

signalling availability of workers (Disdier and Mayer 2004)) have a positive effect of the

success probability. In contrast, the total investment logarithm, greenfield projects and host

country GDP per capita have a negative impact.

As robustness tests we run various additional models excluding from the sample the

host and home countries with the fewer amount of investments to avoid the results being

influenced by outliers. In addition, and despite the collinearity tests show no serious

concerns, we also tested the model removing the score of POLCONV in the host country.

However, the results of these sensitivity tests do not differ significantly from those presented

above2.

5. Discussion and conclusion

2 Results available from the authors upon request.

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In this paper we study the association between differences in policy risk between the

home and the host countries and private participation projects in Latin America. To do so,

we analyze a sample of 3,971 projects in 21 host countries in the Latin American region, led

by investors from 47 home countries. We propose and find empirical evidence that larger

absolute differences in levels of policy risk have a positive effect on the probability of success

of the project. This is due to the fact that investors from home countries where policy risk is

low follow a proactive approach towards policy risk and are able to obtain advantageous

conditions from governments with more political discretion. Simultaneously, and keeping in

mind that policy risk levels are relatively high in Latin America, investors from home

countries where policy risk are even higher can find a more stable and safer environment in

the region.

However, we also argue and empirically validate that this significant effect of

distance in policy risk only takes place when the absolute value of distance is taking into

account. When the nominal distance is employed, the positive and negative distances

between home and host countries cancel the effect of each other. This might be in fact the

reason why previous research has found mixed findings regarding the relationship between

distance and performance (Trapczynski and Banalieva, 2016).

We believe that this study makes some relevant contributions. First, we contribute to

the literature on private participation projects (Doh, 2000; Ramamurti, 2003; Doh et al. 2004;

Inoue, 2013; Jiang et al. 2013) by showing that it is the distance between the levels of risk

between the host and the home country, and not so much the level of risk of the host location,

what matters for the success of private participation projects. Second, we also contribute to

the non-market literature and, more precisely, to the literature on the proactive approach to

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political and policy risk and political capabilities (García-Canal and Guillén, 2008; Holburn

and Zelner, 2010; Jiménez, 2011; Jiménez et al. 2014), by empirically showing that higher

distance in the levels of policy risk between the home and the host countries are positively

associated to a higher probability of success in private participation projects in Latin

America, a region so far neglected in previous studies. Finally, our paper also contributes to

the nascent literature on the role of distance as a pivotal variable in international management

(Zaheer et al. 2012) by underlining the importance of distinguishing between distance in

absolute and nominal terms, and by pointing to the potential confounding effect that negative

and positive distances can have on each other if the research setting is not properly defined

(Hernandez and Nieto, 2015; Trapczynski and Banalieva, 2016).

Also, as managerial implications, our results highlight the fact that managers should

be aware and ready to exploit the potential positive aspects of distance, and embrace the

perspective that differences do not always engender negative outcomes for the firm (Tung

and Verbeke, 2010; Stahl and Tung, 2014; Stahl et al. 2016). In addition, our paper also

emphasizes that managers should pay special attention to the analysing the differences

between the home and host countries and not only to the specific characteristics of the host

environment.

We must acknowledge that our paper is subject to some limitations. First of all, our

sample only covers countries in Latin America, so any extrapolation of the results to other

regions must be done with caution. An interesting avenue of future research could lie in

testing the generalizability of our results to other geographic contexts. Second, we are unable

to test the internal political capabilities of companies involved in the projects to deal with

host governments. Despite recent advances on the theoretical conceptualization of political

capabilities (Lawton and Rajwani, 2011; Lawton et al. 2013), their tacit measure and

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sensitive nature makes them difficult to either directly measure or collect through surveys

(Jiménez et al. 2014). Finally, we must acknowledge that we only analyze the impact of

distance in policy risk on the success of private participation projects. Future studies could

enlarge the scope and investigate the impact on other variables related to private participation

projects such as financial performance or levels of equity ownership. Notwithstanding these

limitations, we hope our paper encourages other researchers to conduct further studies and

enlarge our understanding of private participation projects.

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26

Table 1a. List of host countries.

Table 1b. List of home countries.

Argentina Germany Panama

Australia Guatemala Peru

Belgium Honduras Philippines

Bermuda Hong Kong Portugal

Bolivia India Russia

Brazil Ireland Singapore

Canada Israel Spain

Chile Italy Sweden

China Jamaica Switzerland

Colombia Japan United Arab Emirates

Costa Rica Korea (Rep) United Kingdom

Cuba Luxembourg United States

Denmark Mexico Uruguay

Dominican Rep. Netherlands Venezuela

Ecuador Nicaragua Vietnam

France Norway

Argentina

Bolivi

Dominican Rep. Mexico

Bolivia Ecuador Nicaragua

Brazil El Salvador Panama

Chile Guatemala Paraguay

Colombia Haiti Peru

Costa Rica Honduras Uruguay

Cuba Jamaica Venezuela

Argentina

Bolivi

Dominican Rep. Mexico

Bolivia Ecuador Nicaragua

Brazil El Salvador Panama

Chile Guatemala Paraguay

Colombia Haiti Peru

Costa Rica Honduras Uruguay

Cuba Jamaica Venezuela

Argentina

Bolivi

Dominican Rep. Mexico

Bolivia Ecuador Nicaragua

Brazil El Salvador Panama

Chile Guatemala Paraguay

Colombia Haiti Peru

Costa Rica Honduras Uruguay

Cuba Jamaica Venezuela

27

Table 2. Descriptive statistics

Variable Obs Mean Std. Dev. Min Max

Successful 3971 0.918 0.273 0 1

Energy 3971 0.466 0.498 0 1

Telecom 3971 0.266 0.442 0 1

Transport 3971 0.193 0.394 0 1

Water Sewerage 3971 0.074 0.262 0 1

Total investment (log) 3971 3.710 2.101 -3.50 11.98

Age 3971 9.873 5.616 1 24

Delay 3971 4.934 5.563 -3 23

Publicly traded 3971 0.110 0.313 0 1

Greenfield 3971 0.423 0.494 0 1

Host government ownership 3971 0.259 0.438 0 1

Host country POLCONV 3971 0.561 0.226 0 0.89

GDP (log) 3971 11.42 0.692 9.24 12.42

GDP growth 3971 3.703 3.485 -14.88 18.29

GDP Capita (log) 3971 3.689 0.275 2.45 4.18

Unemployment 3971 7.723 4.301 0 20.1

Foreign main investor 3971 0.595 0.490 0 1

Local investor presence 3971 0.482 0.499 0 1

Nominal Distance POLCONV 3971 0.128 0.249 -0.78 0.86

Absolute Distance POLCONV 3971 0.172 0.220 0 0.86

28

Table 3a. Correlation matrix and VIFs (with absolute distance)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 VIF

1. Successful 1

2. Energy -0.01 1

4.64

3. Telecom 0.12 -0.56 1

5.09

4. Transport -0.08 -0.45 -0.29 1

3.03

5. Total investment

(log)

-0.09 -0.01 0.12 -0.00 1

1.26

6. Age 0.10 -0.06 -0.07 0.11 -0.05 1

1.9

7. Delay 0.19 -0.03 0.35 -0.22 -0.08 -0.44 1

2

8. Publicly traded 0.09 -0.09 0.22 -0.09 0.14 0.04 0.22 1

1.25

9. Greenfield -0.10 -0.01 0.30 -0.22 0.10 -0.20 -0.10 -0.17 1

1.66

10. Host government

ownership

0.05 0.37 -0.20 -0.19 0.00 0.07 0.04 -0.02 -0.26 1

1.34

11. Host country

POLCONV

0.02 0.14 -0.24 0.05 0.10 0.13 -0.23 -0.00 -0.1 0.09 1

2.49

12. GDP (log) 0.05 0.18 -0.28 0.06 0.18 -0.27 0.01 0.08 -0.09 0.08 0.43 1

2.85

13. GDP growth -0.01 0.08 -0.03 -0.04 0.06 -0.08 0.06 -0.00 0.03 0.02 -0.13 -0.02 1

1.16

14. GDP capita (log) -0.04 0.14 -0.17 0.02 0.21 -0.45 0.17 0.10 0.03 -0.03 0.22 0.69 0.13 1

2.65

15. Unemployment 0.08 0.04 -0.03 -0.04 -0.11 0.26 -0.01 0.05 -0.16 0.04 0.03 -0.16 -0.23 -0.08 1

1.25

16. Foreign main

investor

0.02 0.04 0.28 -0.27 0.13 -0.01 0.15 0.03 0.13 0.05 -0.20 -0.35 0.04 -0.15 0.05 1

4.7

17. Local investor

presence

-0.01 -0.07 -0.24 0.27 -0.11 0.02 -0.12 0.01 -0.11 -0.08 0.16 0.30 -0.04 0.15 -0.02 -0.85 1

3.79

18. Absolute Distance

POLCONV

0.02 -0.05 0.27 -0.17 0.00 -0.09 0.21 0.00 0.13 -0.04 -0.62 -0.40 0.09 -0.22 -0.00 0.6 -0.53 1 2.78

29

Table 3b. Correlation matrix and VIFs (with nominal distance)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 VIF

1. Successful 1

2. Energy -0.00 1

4.64

3. Telecom 0.12 -0.56 1

5.12

4. Transport -0.08 -0.45 -0.29 1

3.03

5. Total investment

(log)

-0.09 -0.01 0.12 -0.00 1

1.26

6. Age 0.10 -0.06 -0.07 0.11 -0.05 1

1.9

7. Delay 0.19 -0.03 0.35 -0.22 -0.08 -0.44 1

2.03

8. Publicly traded 0.09 -0.09 0.22 -0.09 0.14 0.04 0.22 1

1.25

9. Greenfield -0.10 -0.01 0.30 -0.22 0.10 -0.20 -0.10 -0.17 1

1.66

10. Host government

ownership

0.05 0.37 -0.20 -0.19 0.00 0.07 0.04 -0.0 -0.26 1

1.33

11. Host country

POLCONV

0.02 0.14 -0.24 0.05 0.10 0.13 -0.23 -0.00 -0.10 0.09 1

2.08

12. GDP (log) 0.05 0.18 -0.28 0.06 0.18 -0.27 0.01 0.08 -0.09 0.08 0.43 1

2.85

13. GDP growth -0.01 0.08 -0.03 -0.04 0.06 -0.08 0.06 -0.00 0.03 0.02 -0.13 -0.02 1

1.16

14. GDP capita (log) -0.04 0.14 -0.17 0.02 0.21 -0.45 0.17 0.10 0.03 -0.03 0.22 0.69 0.13 1

2.63

15. Unemployment 0.08 0.04 -0.03 -0.04 -0.11 0.26 -0.01 0.05 -0.16 0.04 0.03 -0.16 -0.23 -0.08 1

1.26

16. Foreign main

investor

0.02 0.04 0.28 -0.27 0.13 -0.01 0.15 0.03 0.13 0.05 -0.20 -0.35 0.04 -0.15 0.05 1

4.36

17. Local investor

presence

-0.01 -0.07 -0.24 0.27 -0.11 0.02 -0.12 0.01 -0.11 -0.09 0.16 0.30 -0.04 0.15 -0.02 -0.85 1

3.8

18. Nominal Distance

POLCONV

0.00 0.00 0.14 -0.13 -0.06 -0.08 0.24 -0.00 0.04 -0.00 -0.63 -0.32 0.08 -0.16 -0.02 0.41 -0.33 1 2.03

30

Table 4. Logit regression result

(1) (2)

VARIABLES SUCCESFUL SUCCESFUL

Energy 0.986*** 0.975***

(0.230) (0.230)

Telecom 1.998*** 1.945***

(0.329) (0.329)

Transport 0.470** 0.442**

(0.219) (0.220)

Total Investment (Log) -0.164*** -0.159***

(0.0369) (0.0367)

Age 0.103*** 0.103***

(0.0148) (0.0149)

Delay 0.159*** 0.162***

(0.0219) (0.0220)

Publicly Traded 0.851* 0.830*

(0.479) (0.478)

Greenfield -0.514*** -0.500***

(0.166) (0.166)

Host Government Participation -0.298* -0.295*

(0.177) (0.177)

Host country POLCONV 0.621 0.109

(0.380) (0.376)

GDP (Log) 1.195*** 1.214***

(0.142) (0.142)

GDP Growth 0.0335 0.0333

(0.0210) (0.0210)

GDP per capita (Log) -1.021*** -1.086***

(0.378) (0.377)

Unemployment 0.0577*** 0.0584***

(0.0168) (0.0168)

Foreign Sponsor Main 0.197 0.456

(0.293) (0.282)

Local investor presence 0.180 0.154

(0.267) (0.267) Absolute Distance POLCONV 1.130**

(0.478) Nominal Distance POLCONV -0.0800

(0.339)

Constant -10.08*** -9.732***

(1.441) (1.433)

Log likelihood -879.24*** -882.05***

Pseudo R2 0.2149 0.2124

Observations 3,971 3,971

31

Table 5. Average marginal effects

(1) (2)

VARIABLES SUCCESFUL SUCCESFUL

Energy 0.0619*** 0.0614***

(0.0144) (0.0144)

Telecom 0.125*** 0.122***

(0.0206) (0.0207)

Transport 0.0295** 0.0278**

(0.0138) (0.0138)

Total Investment (Log) -0.0103*** -0.0100***

(0.00231) (0.00230)

Age 0.00648*** 0.00648***

(0.000919) (0.000927)

Delay 0.00995*** 0.0102***

(0.00139) (0.00140)

Publicly Traded 0.0534* 0.0523*

(0.0301) (0.0301)

Greenfield -0.0323*** -0.0315***

(0.0104) (0.0105)

Host Government Participation -0.0187* -0.0186*

(0.0111) (0.0112)

Host country POLCONV 0.0390 0.00684

(0.0239) (0.0237)

GDP (Log) 0.0750*** 0.0764***

(0.00882) (0.00881)

GDP Growth 0.00211 0.00209

(0.00131) (0.00132)

GDP per capita (Log) -0.0641*** -0.0683***

(0.0237) (0.0237)

Unemployment 0.00362*** 0.00367***

(0.00105) (0.00106)

Foreign Sponsor Main 0.0123 0.0287

(0.0184) (0.0177)

Local investor presence 0.0113 0.00970

(0.0168) (0.0168) Absolute Distance POLCONV 0.0709**

(0.0300) Nominal Distance POLCONV -0.00503

(0.0213)

Observations 3,971 3,971